Big Data has 3 important dimensions – Volume, Variety and Velocity to add Value to the organizations. In the article data Variety dimension will be explored and discussed to understand how Big Data adds value the organizations.
Data Variety can be segmented into two groups –Structured Data such as customer age, income and name, and Un-structured Data such as customer calls, emails, social media comments and tweets, and videos.
The unstructured data can be used to augment the information availability to generate more accurate insights, or it can be used on its own to generate insights. The big data analytics plays an important role in both the aspects.
Organization can supplement structured data analytics with unstructured data & analytics. Customer calls are recorded and stored only to increase the expenditure. The structured part of the customer calls – number of times customer and what time/days a customer called- are used mainly. The central or most important information – why a customer called? Was the customer satisfied with the response? – is not analyzed and actioned upon.
The big data analytics on unstructured data can help in answering the questions which otherwise would have been significantly difficult or impossible to answer. The unstructured data analytics will help in detecting common concerns of the customers, identifying opportunities in terms of providing right product and services, and servicing the customers effectively.
Steps used in Unstructured Data Analytics
Unstructured Data is analyzed using text analytics and big data tools to identify categories and key words based on the data. The categories and key words information can be converted into structured data for additional analysis, or can be analyzed to identify the patterns and trends. The identified patterns and trends can be explored to develop actionable insights.
An example of generating actionable insights using calls data
For a bank, the customer calls data were analyzed to identify reasons of customer closing a product. All the customer calls which were related to the product were segregated for the analysis. Based on verbatim spoken during the calls, the calls data were analyzed to identify the key words. The key words which were related to customer closing the product are grouped together. The key word categories and could were analyzed. The analysis has helped in identifying top 5 reasons of customers closing the product and helped the management in taking appropriate actions – better communication on product features and their value to the customers.